Yazar "Yildizel, Sadik Alper" seçeneğine göre listele

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    Application of waste ceramic powder as a cement replacement in reinforced concrete beams toward sustainable usage in construction
    (Elsevier, 2023) Aksoylu, Ceyhun; Oztilic, Yasin Onuralp; Bahrami, Alireza; Yildizel, Sadik Alper; Hakeem, Ibrahim Y.; Ozdoner, Nebi
    The main purpose of this study was to investigate the flexural behavior of reinforced concrete beams (RCBs) containing waste ceramic powder (CP) as partial replacement of cement. For this purpose, flexural tests were carried out using various amounts of mixing ratios. By determining the amount of CP utilized in the optimum ratios, it was aimed both to make predictions for design engineers and to show its beneficial effect on the environment by recycling the waste material. For this purpose, twelve specimens were produced and verified to monitor the flexural behavior. The longitudinal reinforcements percentage (0.77%, 1.21%, and 1.74%) and CP percentage (0%, 10%, 20%, and 30%) were chosen as the parameters. CP could be effectively used up to 10% of cement as a replacement material. Increasing the CP percentage by more than 10% could considerably reduce the load-carrying capacity, ductility, and stiffness of RCBs, specifically when the longitudinal reinforcements percentage was high. In other words, as CP increased from 0% to 30%, the load-carrying capacity decreased between 0.4% and 27.5% compared with RCBs with the longitudinal tension reinforcements of 2 phi 8 without CP. However, reductions of 5.5-39.8% and 2.15-39.5% in the load-carrying capacity occurred respectively compared with RCBs with the longitudinal tension reinforcements of 2 phi 10 and 2 phi 12 without CP. The achieved longitudinal reinforcements percentage was close to the balanced ratio, while more than 10% CP cannot be used without any precautions for mixtures.
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    Predicting Compressive Strength of Color Pigment Incorporated Roller Compacted Concrete via Machine Learning Algorithms: A Comparative Study
    (Springernature, 2023) Calis, Gokhan; Yildizel, Sadik Alper; Keskin, Ulku Sultan
    Due to its low albedo, traditional asphalt pavement contributes to the urban heat island effect. Color pigment added roller compacted high performance concrete is a novel approach to reducing the urban heat island effect through the use of paving materials. In this study, color pigment added roller compacted concrete specimens were produced and evaluate via the machine learning algorithms. Predicting compressive strength of concrete by utilization of machine learning methods is highly preferred method by scholars and professionals since ingredients' resources are intensive and time consuming. This research focused to predict the compressive strength of color pigment incorporated roller compacted concrete by applying multiple linear regression (ML), gradient boosting (GB), random forest (RF), support vector machines (SVM), artificial neural network (ANN) and bagging algorithms (BGG). A comprehensive database containing coarse aggregates, fine aggregate, water, cement and pigment amounts and density, age information as input parameters. The analysis results reveal that Bagging algorithm was able to obtain more satisfactory results than the other algorithms in predicting compressive strength (CS) of color pigment incorporated roller compacted concrete. In this algorithm, root mean square error (RMSE) was determined to be 1.53, R-2 to be 0.962, mean absolute error (MAE) to be 0.916, and mean absolute percentage error (MAPE) to be 0.033. ANN algorithm showed significant accuracy in prediction process with RMSE of 1.725, R-2 of 0.949, MAE of 1.144, and MAPE of 0.040. The lowest accuracy was obtained in SVM algorithm with RMSE of 26.910 R-2 of 0.512, MAE of 3.981, and MAPE of 0.040. Therefore, the present study can provide an efficient option for estimating the of color added Roller compacted concrete for pavements.